TY  - CONF
AU  - State, Laura
AU  - Vilimelis Aceituno, Pau
A3  - Tetko, Igor V.
A3  - Kůrková, Věra
A3  - Karpov, Pavel
A3  - Theis, Fabian
TI  - Training Delays in Spiking Neural Networks
M1  - FZJ-2020-00875
SN  - 978-3-030-30486-7 (print)
PY  - 2019
AB  - Spiking Neural Networks (SNNs) are a promising computational paradigm, both to understand biological information processing and for low-power, embedded chips. Although SNNs are known to encode information in the precise timing of spikes, conventional artificial learning algorithms do not take this into account directly. In this work, we implement the spike timing by training the synaptic delays in a single layer SNN. We use two different approaches: a classical gradient descent and a direct algebraic method that is based on a complex-valued encoding of the spikes. Both algorithms are equally able to correctly solve simple detection tasks. Our work provides new optimization methods for the data analysis of highly time-dependent data and training methods for neuromorphic chips.
T2  - ICANN 2019: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation pp
CY  - 17 Sep 2019 - 19 Sep 2019, Munich (Germany)
Y2  - 17 Sep 2019 - 19 Sep 2019
M2  - Munich, Germany
LB  - PUB:(DE-HGF)1 ; PUB:(DE-HGF)3
UR  - <Go to ISI:>//WOS:000546494000054
DO  - DOI:10.1007/978-3-030-30487-4_54
UR  - https://juser.fz-juelich.de/record/873632
ER  -